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Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing. [ABSTRACT FROM AUTHOR]

Food safety has garnered much worldwide attention recently for reasons that are, unfortunately, not always positive. Traceability system (TS) is designed to assure safe and good quality food, while reducing the costs of food recalls. It should encompass all stakeholders, including governments, companies, and consumers, each of whom has an important role in the implementation and guardianship of such systems. The EU and China are amongst the main players implementing TS and are constantly exploring new opportunities and monitoring challenges for TS in a time of shifting consumer demands and rapid new technology innovation. This article states development stages from TS 1.0 to 3.0. and reviews TS development in a number of key countries and regions. Comparisons between the EU and China are drawn in terms of government, corporate, and consumer involvement in traceability. A functional TS, while providing bi-directional communication between trading partners, must meet the laws and regulations where it operates. A functional system must also consider consumer value and perception, which varies with geography. There are a variety of promising technologies available on the market today to modernize TS, including artificial intelligence (AI) and blockchain. A key finding of this research is that both the EU and China have developed significant trade links in recent years which will certainly positively impact both economies. Key to underpinning the sustainability of these trade links will be the adoption of common TS to prevent negative associations. • Present a comprehensive analysis framework from government, company and customer perspectives. • Comparative review between the EU and China. • Outline the trend of traceability development with the AI and blockchain technologies. • Highlight the strategic technological and consumer-based requirements for global scale traceability systems. • Provide multi-stakeholder perspectives and inputs of traceability. [ABSTRACT FROM AUTHOR]

Abstract Radio frequency identification (RFID) and quick response (QR) codes are an effective way to identify traceable resource units (TRUs) and are widely applied in traceability systems. TRU transformation is common for things such as beef segmentation and wheat-flour packaging. The present study describes the development and testing of equipment for barcode-RFID bidirectional transformation to improve traceability by conserving identification association and information correspondence while transforming barcodes to RFID and vice versa. The framework of traceable bidirectional identification labels is based on comparing the features of RFID and barcodes and on analyzing TRU transformation. The proposed equipment includes a tactile industrial controller, a RFID-reader module, an embedded printing module, and a barcode scanning module. Furthermore, we develop the main functions of RFID-barcode conversion, bar-code-RFID conversion, and condition monitoring. The system is tested in field experiments based on two typical scenarios: beef segmentation and wheat-flour packaging. The average conversion success rate in RFID-barcode processing is 97%, and in barcode-RFID processing is 93.48%. The proposed equipment is more rapid than the reference method: 3.2 versus 8.7 s for RFID-barcode transformation and 10.5 versus 13.3 s for barcode-RFID transformation. In addition, the proposed equipment is less expensive than the conventional equipment. Test results indicate that continuous traceability is improved because identification association and information correspondence are conserved after TRU transformation. Hightlights • Proposed a method to improve continuous traceability of food stuff by TRUs relating. • Developed a barcode-RFID transformation equipment to implement the proposed method. • Performed an experiments on two typical scenario to validate the feasibility. [ABSTRACT FROM AUTHOR]

Recent legal requirements and market demands have motivated more food companies to implement traceability systems. Ensuring safe farming practices is the first step in food supply chain traceability, and reasonable pesticide use is a main feature of food safety and sustainable production. This study describes the design and development of a cloud-based platform for rational pesticide use to guarantee the source safety of traceable vegetables. The system includes a pesticide use control cloud platform (PUCC) and a pesticide user application (PUA), which interactively guide users through the steps of pesticide purchasing, pesticide application, harvest time, and pesticide evaluation. Models for evaluating and recommending potential pesticides were developed based on an open library of pesticide use rules. The PUCC, which includes the main functions of farmer registration, authentication of platform administrator, and information management for plant protection service agencies, was developed using Microsoft Visual Studio 2010 and deployed on the Internet. The PUA provides interfaces for pesticide purchasing guideline, pesticide application, optimal harvest time, and feedback. As a case study, the system was used for about a year in 24 vegetable bases in Tianjin. The effectiveness of the system was evaluated by investigating 8 management center staff members and 41 farmers. Management agencies noted the positive effects of promoting reasonable pesticide use, facilitating information accessibility, and enhancing management. Advantages to farmers included reducing the risk of unreasonable pesticide usage, decreasing the risk of counterfeit pesticides, and improving vegetable quality and safety; disadvantages included increased costs and reduced efficiency. In addition, the system improved external and internal traceability to ensure crop quality and safety. [ABSTRACT FROM AUTHOR]

Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45 mm reading distance, 34.58 mm code size, 100 bytes coded characters and 2.98 m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision. [ABSTRACT FROM AUTHOR]

Recent developments in the legal establishment and the market have motivated more agro-food companies to implement traceability systems (TS). TS play an important role not only for planning system implementation before development, but also for analyzing system performance after using the system. A novel agro-food TS model is presented here, based on comprehensive and quantifiable granularity concepts. A 2-layer index system was established; the first layer was mainly factors such as precision, breadth, and depth, and the second layer included seven indicator sub-factors: external trace units, internal flow units, IU conversion, information collection content, information update frequency, forward tracking distance, and backward tracing distance. An indicator’s overall score was scaled with five contributing scores that graded the assignment method. Indicator weight was confirmed with the AHP method. The weight values of the seven indicators were 0.1985, 0.1141, 0.0872, 0.1870, 0.1248, 0.1442, and 0.1442, respectively. A weighted sum model was adopted to calculate the evaluation value. A high evaluation value indicated high granularity. The granularity model was applied in two enterprises, here identified as WPF and WFPE, which were located at different stages in wheat-flour supply chain. The survey results showed that WFPE should invest more in tracing equivalent granularity than WPF should because it involves multi-stage processing, a complicated supply chain structure, it is a large enterprise, and operates in a strict regulatory environment. Furthermore, WFPE was motivated to implement a high granularity level because of benefits in supply chain management, market and customer response, and recall and risk management. In the future, an updated granularity evaluation model that could combine enterprise characteristics and uncover hidden costs and benefits will be studied further. [ABSTRACT FROM AUTHOR]

In order to protect the quality and safety of products of the fresh cut vegetable processing and improve fresh cut vegetable production and processing efficiency, a fresh cut vegetable processing batch mixing optimization model was put forward, which can reduce the recall cost resulted from the mixed batches during the manufacture. The batch-mixing phenomenon of raw materials often occurs during the production process of fresh-cut vegetables. On the other hand, different batches of materials have different properties which will exert some effects on the rate of the production yield. Proper allocation of the materials' batches will reduce the recall cost of defective products. In order to enhance the ability of production efficiency and improve the supervision on the quality and safety for fresh-cut vegetables, an optimized model for this problem should be developed. In this research, after the analysis of the basic process in fresh-cut vegetables manufacture, a model based on the order of the daily manufacture in enterprise and the batch of materials was established, which could reduce the batch-mixing level of the materials and improve the production efficiency. The manufacture process of the fresh-cut vegetables was taken as the core research point, and the experiment parameters were obtained from a cooperative enterprise in Beijing which was a typical manufacture enterprise of fresh-cut vegetables. Unlike other areas of vegetable manufacture, the manufacture of fresh-cut vegetables needed to take the different products and different batches of material processing into account. According to the actual production requirements of small and medium-sized fresh-cut vegetable processing enterprises, the production process, which could be well applicable to the pattern of single material warehouse and single warehouse of finished product, was studied in details. Based on the reality limits, the orders' sequence and the batch of materials should be optimized. The processing of product order and the selection and sorting of raw material batch belong to the NP-Hard problem, and the scale of the problem rapidly increased with the expanding of the order and batch. So it was necessary to adopt intelligent optimization methods to solve the problem. And based on the actual condition in the manufacture enterprises of fresh-cut vegetables, the processing sequence of the manufacture order and the selection sequence of the different batches of materials were optimized using genetic algorithm. The population size was set to 100, two-point crossover rate was set to 0.9, the mutation rate was set to 0.1, the weight of material mixing was set to 0.3, the average yield for the processing was set to 0.7 and the maximum amount of iterations was limited to 50. The objective function consisted of the average recall size and the average yield of product. The model was applied in a manufacture enterprise of fresh-cut fruits and vegetables, and the test results showed that the value of the objective function was improved by 10.5%, which could reduce the average recall rate and improve the production efficiency. This model can provide suggestion of material batch distribution and optimization of production for the small and medium-sized fresh-cut vegetable processing enterprises. [ABSTRACT FROM AUTHOR]

The detection information of farm products increases rapidly, and plays an important role in traceability. The existed detection processing method used the mode of testing sample with detection instruments and manual recording data. This mode leads to the hazard of unassociated batches and distorted information, so the direct and credible effects of detection information in the traceability content are needed. In this paper, the reasons why detection information is not able to be integrated in the existed traceability system were analyzed. Modified system framework with detection information extracting and identification was proposed, which combined the rapid real-time detection instrument for vegetable pesticide residues mainly using enzyme inhibition method. In the detection process, information including detection ID, farm product batch and inhibition rate was recorded. In order to implement the automation reading and extracting of detection information, the access flowchart of detection data included the steps of instrument connection, vegetable detection, data extraction and data storing. The correlation between vegetable plant batch, detection identification number and product traceability code was the other important step for detection information tracing. Because one vegetable plant batch may be divided into many package units, the relationship between the batch code of vegetable plants and product traceability code was the type of 'one to many'. Because of the similar characteristics in one plant batch, the same batch of vegetable was detected once and the relationship between vegetable plant batch code and detection identification code was the type of 'one to one'. The batch correlation between product traceability code and detection identification code was established with the link of vegetable plant batch code. Based on .Net platform and the existed vegetable safety production management system, the vegetable production management and traceability system was updated for three functions. The detection instrument linked the vegetable production management and traceability system with serial port, and linking self-checking was implemented in the instrument link function. Then detection data was obtained through special data format. The detection data could be displayed but not modified in the data management function. The updated system was applied in 153 vegetable production bases in Tianjin city. During March 1st to May 30th in 2014, 28038 vegetable samples were detected and data obtain success rate with the updated system was 100%, and the data acquirement efficiency was improved. Furthermore, the detection results of 20 samples were compared between rapid real-time detection by enterprise self-check and casual inspection by supervision departments. The 20 samples were divided into two groups with No.1-8 samples using the manual recording mode of detection data without the updated the system and No.9-20 obtaining the detection data by automatic reading with the updated the system. The application results showed that the data acquisition success rate was 100% and the difference between the enterprise self-check value and the supervision casual inspection value was low with the absolute value of 4.37 after using the updated the system. The detection value with manual recording of No.3 sample was obviously lower than the one by supervision departments, which has the possible of distort data. Furthermore, the traceability results for using and not using the updated system were compared. Through using the system, the traceability results could indicate the precision detection information through the batches' correlation. The study in this paper presents an important step for satisfying the deeper requirement in traceability system. [ABSTRACT FROM AUTHOR]

Cold chains are effective in maintaining food quality and reducing food losses, especially for long-distance international food commerce. Several recent reports have demonstrated that frozen foods are serving as carriers of SARS-CoV-2 and transmitting the virus from one place to another without any human-to-human contact. This finding highlights significant difficulties facing efforts to control the spread of COVID-19 and reveal a transmission mechanism that may have substantially worsened the global pandemic. Traditional food cold chain management practices do not include specific procedures related to SARS-CoV-2-related environmental control and information warnings; therefore, such procedures are urgently needed to allow food to be safely transported without transmitting SARS-CoV-2. In this study, a conjoint analysis of COVID-19 and food cold chain systems was performed, and the results of this analysis were used to develop an improved food cold chain management system utilizing internet of things (IoT) and blockchain technology. First, 45 COVID-19-related food cold chain incidents in China, primarily involving frozen meat and frozen aquatic products, were summarized. Critical food cold chain control points related to COVID-19 were analyzed, including temperature and cold chain requirements. A conceptual system structure to improve food cold chain management, including information sensing, chain linking and credible tracing, was proposed. Finally, a prototype system, which consisted of cold chain environment monitoring equipment, a cold chain blockchain platform, and a food chain management system, was developed. The system includes: 1) a defining characteristic of the newly developed food cold chain system presented here is the use of IoT technology to enhance real-time environmental information sensing capacity; 2) a hybrid data storage mechanism consisting of off-chain and on-chain systems was applied to enhance data security, and smart contracts were used to establish warning levels for food cold chain incidents; and 3) a hypothetical food cold chain failure scenario demonstration in which information collection, intelligent decision making, and cold chain tracing were integrated and automatically generated for decision-making. By integrating existing technologies and approaches, our study provides a novel solution to improve traditional food cold chain management and thus meet the challenges associated with the COVID-19 pandemic. Although our system has been shown to be effective, subsequent studies are still required to develop precise risk evaluation models for SARs-CoV-2 in food cold chains and more precisely control the entire process. By ensuring food safety and reliable traceability, our system could also contribute to the formulation of appropriate mechanisms for international cooperation and minimize the effect of the COVID-19 pandemic on international food commerce. Check critical cold chain control points related to COVID-19 and environment. Credible cold chain traceability combining IOT and blockchain technology. Improve traditional food cold chain management under global COVID-19 pandemic. [ABSTRACT FROM AUTHOR]